This invention relates to magnetic resonance imaging (MRI) systems and more particularly to such systems used for acquiring data and reconstructing data for two- or three-dimensional images.
Magnetic Resonance imaging (MRI) involves acquiring data in the spatial frequency domain referred to as k-space, and transforming the data into the spatial domain prior to viewing. The acquired k-space data samples have both magnitude and phase components. The Fourier transform of the k-space data is the MRI image.
Cartesian sampling in k-space followed by inverse discrete Fourier transform (IDFT) represents a commonly-used magnetic resonance imaging scheme. While the IDFT reconstruction is generally realized using the well-known and highly efficient fast Fourier transform (FFT) algorithm, reconstruction latency can still be significant. Reconstruction latency refers to the interval from the time of data acquisition to the time of actual visualization of the corresponding image.
With a modern MR imaging system run in gated time-resolved, interleaved multi-slice or volumetric imaging mode, depending on the data set size the latency may be on the order of tens of seconds, which may seriously hamper the use of the system for real-time or concurrent monitoring and diagnosis. The conventional practice of separating the FFT-reconstruction from the acquisition of a complete set of k-space samples is a main contributor to the latency. Further, the problem is generally aggravated when spatiotemporal coverage/resolution increases, because the time required to complete an FFT increases as the number of data points increases.
With the MR system run in fluoroscopy mode, the reconstruction latency degrades the system""s real-time performance and leads to low image frame rate. In this case, computation redundancy may be another major contributor to the latency. To achieve a smoother depiction of imaged dynamics for example, the known technique of sliding-window reconstruction attempts to increase the number of reconstructed images through sharing raw data between reconstructed images. When this technique is applied in Cartesian-sampling based MR fluoroscopy, data acquisition constantly loops through the phase encodes, resulting in a fully refreshed k-space data frame every Ttraverse seconds (Ttraverse=time required for a complete k-space traversing). Image reconstruction, on the other hand, repeatedly applies FFT to a sliding window of the most recent full set of phase encodes, producing an image every Tcompute seconds (Tcompute=time required for updating an image). While Ttraverse determines the temporal resolution of the fluoroscopic images, Tcompute determines the upper limit of the rate at which one can slide the reconstruction window and hence the frame-rate of the fluoroscopic images. The fluoroscopy""s real-time performance will thus be degraded if the FFT""s are carried out slowly, because not only will the latency be significant, but also the frame-rate will be low.
In many applications where shortening data acquisition time or enhancing temporal resolution is desired, both reduction of Ttraverse (to improve temporal resolution) and minimization of Tcompute (to achieve real-time monitoring capability) are important. Reduction of Ttraverse can be accomplished through utilizing higher speed gradients for example. Under many circumstances where the MR images are real (up to a constant phase factor), another known technique is to reduce k-space coverage, which generally involves acquiring k-space data with a partial k-space traversing, and consequently reducing the data acquisition time or improving temporal resolution of the fluoroscopic images. However, partial k-space traversing presents some data reconstruction challenges. What is needed is a method for speed-enhanced acquisition and latency-reduced reconstruction when partial k-space traversing is employed.
A method of data acquisition and reconstruction for use with a Magnetic Resonance Imaging (MRI) system comprises partially traversing k-space in a plurality of segments, computing sub-images for each of the segments, and incrementally reconstructing a plurality of intermediate and final images from the sub-images on-the-fly. The partial k-space traversing reduces data acquisition time and the incremental reconstruction reduces acquisition-to-visualization latency.